Abstract 5441: Cell cycle arrest status predicted from H&amp;E stained images using deep learning
Authors: Christina Aigner, Brian Reichholf, Maxime Emschwiller, Marija Pezer, Tobias Winterhoff, Simon Schallenberg, Rosemarie Krupar, Lukas Ruff, Sharon Ruane, Maximilian Alber, Frederick Klauschen, Francesca Trapani
Journal: Cancer Research (2023)

Abstract Background: Cyclin-dependent kinase inhibitor p21 is a regulator of cell cycle progression. Due to its capacity to induce cell cycle arrest (CCA) when expressed in the nucleus, it is also considered a tumor suppressor and its presence can be used to evaluate the efficacy of anti-cancer treatment. Since pathologists cannot assess nuclear p21 status of cells using hematoxylin &amp; eosin (H&amp;E) stained tissue alone, the current state-of-the-art approach requires evaluation using immunohistochemistry (IHC). This process is time-consuming, adds additional cost and usually requires a separate section of the sample tissue. Further, manual evaluation of IHC stainings typically shows high inter-pathologist variability. In this study, we developed a deep learning model that predicts cell-level nuclear p21 status on H&amp;E-stained tissue alone, aiming to bypass the IHC-staining step and all drawbacks associated with it. Methods: 99 tissue sections of pancreas cancer xenografts were stained by H&amp;E, then restained for p21 (IHC). The samples originated from mice that had undergone experiments conducted to examine the pharmacodynamic effect of anti-cancer treatments. H&amp;E and IHC image pairs were coregistered to micrometer level precision. A tissue segmentation model was trained to detect regions of ‘carcinoma’ in H&amp;E. This model was used as a filter and only cells within the tumor region were considered for analysis. Individual cells were detected in the H&amp;E image and these locations were transferred to the IHC image. A deep learning model was trained using IHC-informed labels to extract labels at scale from each IHC image. These labels were then transferred to the H&amp;E image and used to train a second deep learning model which predicted nuclear p21 status from H&amp;E alone. Results: IHC-informed labels were extracted with a balanced accuracy (BA) of 0.93. The resulting ‘H&amp;E only’ nuclear p21 model achieved a cell-level BA of 0.83. A case level comparison of the share of predicted p21+ nuclei showed a Pearson correlation of 0.72 with the share of p21+ nuclei determined by the IHC-informed extracted labels. Further, when used to characterize all samples, the model detected significant differences between treatment groups. Conclusion: Nuclear p21 status can be detected at a cellular level in H&amp;E images alone, using a deep learning model. This provides an opportunity to assess samples for cell cycle arrest status at scale in a standardized manner, without the need for IHC staining. Citation Format: Christina Aigner, Brian Reichholf, Maxime Emschwiller, Marija Pezer, Tobias Winterhoff, Simon Schallenberg, Rosemarie Krupar, Lukas Ruff, Sharon Ruane, Maximilian Alber, Frederick Klauschen, Francesca Trapani. Cell cycle arrest status predicted from H&amp;E stained images using deep learning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5441.